A Note on Randomized Kaczmarz Algorithm for Solving Doubly-Noisy Linear Systems
This work addresses a practical limitation in iterative solvers for noisy linear systems, but it is incremental as it extends existing analysis to a more realistic scenario without introducing a new method.
The paper tackles the problem of solving linear systems with noise in both the coefficient matrix and right-hand side vector, analyzing the convergence of the randomized Kaczmarz algorithm for such doubly-noisy systems, showing that convergence depends on a quantity related to the noisy matrix and is controllable under different noise conditions.
Large-scale linear systems, $Ax=b$, frequently arise in practice and demand effective iterative solvers. Often, these systems are noisy due to operational errors or faulty data-collection processes. In the past decade, the randomized Kaczmarz (RK) algorithm has been studied extensively as an efficient iterative solver for such systems. However, the convergence study of RK in the noisy regime is limited and considers measurement noise in the right-hand side vector, $b$. Unfortunately, in practice, that is not always the case; the coefficient matrix $A$ can also be noisy. In this paper, we analyze the convergence of RK for {\textit{doubly-noisy} linear systems, i.e., when the coefficient matrix, $A$, has additive or multiplicative noise, and $b$ is also noisy}. In our analyses, the quantity $\tilde R=\| \tilde A^{\dagger} \|^2 \|\tilde A \|_F^2$ influences the convergence of RK, where $\tilde A$ represents a noisy version of $A$. We claim that our analysis is robust and realistically applicable, as we do not require information about the noiseless coefficient matrix, $A$, and considering different conditions on noise, we can control the convergence of RK. {We perform numerical experiments to substantiate our theoretical findings.}